kube-metrics-adapter

Kube Metrics Adapter is a general purpose metrics adapter for Kubernetes that can collect and serve custom and external metrics for Horizontal Pod Autoscaling.

It supports scaling based on Prometheus metrics, SQS queues and others out of the box.

It discovers Horizontal Pod Autoscaling resources and starts to collect the requested metrics and stores them in memory. It's implemented using the custom-metrics-apiserver library.

Here's an example of a HorizontalPodAutoscaler resource configured to get requests-per-second metrics from each pod of the deployment myapp.

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: myapp-hpa
  annotations:
    # metric-config.<metricType>.<metricName>.<collectorType>/<configKey>
    metric-config.pods.requests-per-second.json-path/json-key: "$.http_server.rps"
    metric-config.pods.requests-per-second.json-path/path: /metrics
    metric-config.pods.requests-per-second.json-path/port: "9090"
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: myapp
  minReplicas: 1
  maxReplicas: 10
  metrics:
  - type: Pods
    pods:
      metric:
        name: requests-per-second
      target:
        averageValue: 1k
        type: AverageValue

The metric-config.* annotations are used by the kube-metrics-adapter to configure a collector for getting the metrics. In the above example it configures a json-path pod collector.

Kubernetes compatibility

Like the support policy offered for Kubernetes, this project aims to support the latest three minor releases of Kubernetes.

The default supported API is autoscaling/v2 (available since v1.23). This API MUST be available in the cluster which is the default.

Building

This project uses Go modules as introduced in Go 1.11 therefore you need Go >=1.11 installed in order to build. If using Go 1.11 you also need to activate Module support.

Assuming Go has been setup with module support it can be built simply by running:

export GO111MODULE=on # needed if the project is checked out in your $GOPATH.
$ make

Collectors

Collectors are different implementations for getting metrics requested by an HPA resource. They are configured based on HPA resources and started on-demand by the kube-metrics-adapter to only collect the metrics required for scaling the application.

The collectors are configured either simply based on the metrics defined in an HPA resource, or via additional annotations on the HPA resource.

Pod collector

The pod collector allows collecting metrics from each pod matched by the HPA. Currently only json-path collection is supported.

Supported metrics

Metric	Description	Type	K8s Versions
custom	No predefined metrics. Metrics are generated from user defined queries.	Pods	>=1.12

Example

This is an example of using the pod collector to collect metrics from a json metrics endpoint of each pod matched by the HPA.

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: myapp-hpa
  annotations:
    # metric-config.<metricType>.<metricName>.<collectorType>/<configKey>
    metric-config.pods.requests-per-second.json-path/json-key: "$.http_server.rps"
    metric-config.pods.requests-per-second.json-path/path: /metrics
    metric-config.pods.requests-per-second.json-path/port: "9090"
    metric-config.pods.requests-per-second.json-path/scheme: "https"
    metric-config.pods.requests-per-second.json-path/aggregator: "max"
    metric-config.pods.requests-per-second.json-path/interval: "60s" # optional
    metric-config.pods.requests-per-second.json-path/min-pod-ready-age: "30s" # optional
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: myapp
  minReplicas: 1
  maxReplicas: 10
  metrics:
  - type: Pods
    pods:
      metric:
        name: requests-per-second
      target:
        averageValue: 1k
        type: AverageValue

The pod collector is configured through the annotations which specify the collector name json-path and a set of configuration options for the collector. json-key defines the json-path query for extracting the right metric. This assumes the pod is exposing metrics in JSON format. For the above example the following JSON data would be expected:

{
  "http_server": {
    "rps": 0.5
  }
}

The json-path query support depends on the github.com/spyzhov/ajson library. See the README for possible queries. It's expected that the metric you query returns something that can be turned into a float64.

The other configuration options path, port and scheme specify where the metrics endpoint is exposed on the pod. The path and port options do not have default values so they must be defined. The scheme is optional and defaults to http.

The aggregator configuration option specifies the aggregation function used to aggregate values of JSONPath expressions that evaluate to arrays/slices of numbers. It's optional but when the expression evaluates to an array/slice, it's absence will produce an error. The supported aggregation functions are avg, max, min and sum.

The raw-query configuration option specifies the query params to send along to the endpoint:

  metric-config.pods.requests-per-second.json-path/path: /metrics
  metric-config.pods.requests-per-second.json-path/port: "9090"
  metric-config.pods.requests-per-second.json-path/raw-query: "foo=bar&baz=bop"

will create a URL like this:

http://<podIP>:9090/metrics?foo=bar&baz=bop

There are also configuration options for custom (connect and request) timeouts when querying pods for metrics:

metric-config.pods.requests-per-second.json-path/request-timeout: 2s
metric-config.pods.requests-per-second.json-path/connect-timeout: 500ms

The default for both of the above values is 15 seconds.

The min-pod-ready-age configuration option instructs the service to start collecting metrics from the pods only if they are "older" (time elapsed after pod reached "Ready" state) than the specified amount of time. This is handy when pods need to warm up before HPAs will start tracking their metrics.

The default value is 0 seconds.

Prometheus collector

The Prometheus collector is a generic collector which can map Prometheus queries to metrics that can be used for scaling. This approach is different from how it's done in the k8s-prometheus-adapter where all available Prometheus metrics are collected and transformed into metrics which the HPA can scale on, and there is no possibility to do custom queries. With the approach implemented here, users can define custom queries and only metrics returned from those queries will be available, reducing the total number of metrics stored.

One downside of this approach is that bad performing queries can slow down/kill Prometheus, so it can be dangerous to allow in a multi tenant cluster. It's also not possible to restrict the available metrics using something like RBAC since any user would be able to create the metrics based on a custom query.

I still believe custom queries are more useful, but it's good to be aware of the trade-offs between the two approaches.

Supported metrics

Metric	Description	Type	Kind	K8s Versions
prometheus-query	Generic metric which requires a user defined query.	External		>=1.12
custom	No predefined metrics. Metrics are generated from user defined queries.	Object	any	>=1.12

Example: External Metric

This is an example of an HPA configured to get metrics based on a Prometheus query. The query is defined in the annotation metric-config.external.processed-events-per-second.prometheus/query where processed-events-per-second is the query name which will be associated with the result of the query. This allows having multiple prometheus queries associated with a single HPA.

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: myapp-hpa
  annotations:
    # This annotation is optional.
    # If specified, then this prometheus server is used,
    # instead of the prometheus server specified as the CLI argument `--prometheus-server`.
    metric-config.external.processed-events-per-second.prometheus/prometheus-server: http://prometheus.my-namespace.svc
    # metric-config.<metricType>.<metricName>.<collectorType>/<configKey>
    metric-config.external.processed-events-per-second.prometheus/query: |
      scalar(sum(rate(event-service_events_count{application="event-service",processed="true"}[1m])))      
    metric-config.external.processed-events-per-second.prometheus/interval: "60s" # optional
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: custom-metrics-consumer
  minReplicas: 1
  maxReplicas: 10
  metrics:
  - type: External
    external:
      metric:
        name: processed-events-per-second
        selector:
          matchLabels:
            type: prometheus
      target:
        type: AverageValue
        averageValue: "10"

Example: Object Metric [DEPRECATED]

Note: Prometheus Object metrics are deprecated and will most likely be removed in the future. Use the Prometheus External metrics instead as described above.

This is an example of an HPA configured to get metrics based on a Prometheus query. The query is defined in the annotation metric-config.object.processed-events-per-second.prometheus/query where processed-events-per-second is the metric name which will be associated with the result of the query.

It also specifies an annotation metric-config.object.processed-events-per-second.prometheus/per-replica which instructs the collector to treat the results as an average over all pods targeted by the HPA. This makes it possible to mimic the behavior of targetAverageValue which is not implemented for metric type Object as of Kubernetes v1.10. (It will most likely come in v1.12).

apiVersion: autoscaling/v2beta1
kind: HorizontalPodAutoscaler
metadata:
  name: myapp-hpa
  annotations:
    # metric-config.<metricType>.<metricName>.<collectorType>/<configKey>
    metric-config.object.processed-events-per-second.prometheus/query: |
      scalar(sum(rate(event-service_events_count{application="event-service",processed="true"}[1m])))      
    metric-config.object.processed-events-per-second.prometheus/per-replica: "true"
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: custom-metrics-consumer
  minReplicas: 1
  maxReplicas: 10
  metrics:
  - type: Object
    object:
      metricName: processed-events-per-second
      target:
        apiVersion: v1
        kind: Pod
        name: dummy-pod
      targetValue: 10 # this will be treated as targetAverageValue

Note: The HPA object requires an Object to be specified. However when a Prometheus metric is used there is no need for this object. But to satisfy the schema we specify a dummy pod called dummy-pod.

Skipper collector

The skipper collector is a simple wrapper around the Prometheus collector to make it easy to define an HPA for scaling based on Ingress or RouteGroup metrics when skipper is used as the ingress implementation in your cluster. It assumes you are collecting Prometheus metrics from skipper and it provides the correct Prometheus queries out of the box so users don't have to define those manually.

Supported metrics

Metric	Description	Type	Kind	K8s Versions
requests-per-second	Scale based on requests per second for a certain ingress or routegroup.	Object	Ingress, RouteGroup	>=1.19

Example

Ingress

This is an example of an HPA that will scale based on requests-per-second for an ingress called myapp.

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: myapp-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: myapp
  minReplicas: 1
  maxReplicas: 10
  metrics:
  - type: Object
    object:
      describedObject:
        apiVersion: networking.k8s.io/v1
        kind: Ingress
        name: myapp
      metric:
        name: requests-per-second
        selector:
          matchLabels:
            backend: backend1 # optional backend
      target:
        averageValue: "10"
        type: AverageValue

RouteGroup

This is an example of an HPA that will scale based on requests-per-second for a routegroup called myapp.

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: myapp-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: myapp
  minReplicas: 1
  maxReplicas: 10
  metrics:
  - type: Object
    object:
      describedObject:
        apiVersion: zalando.org/v1
        kind: RouteGroup
        name: myapp
      metric:
        name: requests-per-second
        selector:
          matchLabels:
            backend: backend1 # optional backend
      target:
        averageValue: "10"
        type: AverageValue

Metric weighting based on backend

Skipper supports sending traffic to different backends based on annotations present on the Ingress object, or weights on the RouteGroup backends. By default the number of replicas will be calculated based on the full traffic served by that ingress/routegroup. If however only the traffic being routed to a specific backend should be used then the backend name can be specified via the backend label under matchLabels for the metric. The ingress annotation where the backend weights can be obtained can be specified through the flag --skipper-backends-annotation.

External RPS collector

The External RPS collector, like Skipper collector, is a simple wrapper around the Prometheus collector to make it easy to define an HPA for scaling based on the RPS measured for a given hostname. When skipper is used as the ingress implementation in your cluster everything should work automatically, in case another reverse proxy is used as ingress, like Nginx for example, its necessary to configure which prometheus metric should be used through --external-rps-metric-name <metric-name> flag. Assuming skipper-ingress is being used or the appropriate metric name is passed using the flag mentioned previously this collector provides the correct Prometheus queries out of the box so users don't have to define those manually.

Supported metrics

Metric	Description	Type	Kind	K8s Versions
requests-per-second	Scale based on requests per second for a certain hostname.	External		>=1.12

Example: External Metric

This is an example of an HPA that will scale based on requests-per-second for the RPS measured in the hostnames called: www.example1.com and www.example2.com; and weighted by 42%.

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: myapp-hpa
  annotations:
    metric-config.external.example-rps.requests-per-second/hostnames: www.example1.com,www.example2.com
    metric-config.external.example-rps.requests-per-second/weight: "42"
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: custom-metrics-consumer
  minReplicas: 1
  maxReplicas: 10
  metrics:
  - type: External
    external:
      metric:
        name: example-rps
        selector:
          matchLabels:
            type: requests-per-second
      target:
        type: AverageValue
        averageValue: "42"

Multiple hostnames per metric

This metric supports a relation of n:1 between hostnames and metrics. The way it works is the measured RPS is the sum of the RPS rate of each of the specified hostnames. This value is further modified by the weight parameter explained below.

Metric weighting based on backend

There are ingress-controllers, like skipper-ingress, that supports sending traffic to different backends based on some kind of configuration, in case of skipper annotations present on the Ingress object, or weights on the RouteGroup backends. By default the number of replicas will be calculated based on the full traffic served by these components. If however only the traffic being routed to a specific hostname should be used then the weight for the configured hostname(s) might be specified via the weight annotation metric-config.external.<metric-name>.request-per-second/weight for the metric being configured.

InfluxDB collector

The InfluxDB collector maps Flux queries to metrics that can be used for scaling.

Note that the collector targets an InfluxDB v2 instance, that's why we only support Flux instead of InfluxQL.

Supported metrics

Metric	Description	Type	Kind	K8s Versions
flux-query	Generic metric which requires a user defined query.	External		>=1.10

Example: External Metric

This is an example of an HPA configured to get metrics based on a Flux query. The query is defined in the annotation metric-config.external.<metricName>.influxdb/query where <metricName> is the query name which will be associated with the result of the query. This allows having multiple flux queries associated with a single HPA.

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: myapp-hpa
  annotations:
    # These annotations are optional.
    # If specified, then they are used for setting up the InfluxDB client properly,
    # instead of using the ones specified via CLI. Respectively:
    #  - --influxdb-address
    #  - --influxdb-token
    #  - --influxdb-org
    metric-config.external.queue-depth.influxdb/address: "http://influxdbv2.my-namespace.svc"
    metric-config.external.queue-depth.influxdb/token: "secret-token"
    # This could be either the organization name or the ID.
    metric-config.external.queue-depth.influxdb/org: "deadbeef"
    # metric-config.<metricType>.<metricName>.<collectorType>/<configKey>
    # <configKey> == query-name
    metric-config.external.queue-depth.influxdb/query: |
        from(bucket: "apps")
          |> range(start: -30s)
          |> filter(fn: (r) => r._measurement == "queue_depth")
          |> group()
          |> max()
          // Rename "_value" to "metricvalue" for letting the metrics server properly unmarshal the result.
          |> rename(columns: {_value: "metricvalue"})
          |> keep(columns: ["metricvalue"])        
    metric-config.external.queue-depth.influxdb/interval: "60s" # optional
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: queryd-v1
  minReplicas: 1
  maxReplicas: 4
  metrics:
  - type: External
    external:
      metric:
        name: queue-depth
        selector:
          matchLabels:
            type: influxdb
      target:
        type: Value
        value: "1"

AWS collector

The AWS collector allows scaling based on external metrics exposed by AWS services e.g. SQS queue lengths.

AWS IAM role

To integrate with AWS, the controller needs to run on nodes with access to AWS API. Additionally the controller have to have a role with the following policy to get all required data from AWS:

PolicyDocument:
  Statement:
    - Action: 'sqs:GetQueueUrl'
      Effect: Allow
      Resource: '*'
    - Action: 'sqs:GetQueueAttributes'
      Effect: Allow
      Resource: '*'
    - Action: 'sqs:ListQueues'
      Effect: Allow
      Resource: '*'
    - Action: 'sqs:ListQueueTags'
      Effect: Allow
      Resource: '*'
  Version: 2012-10-17

Supported metrics

Metric	Description	Type	K8s Versions
sqs-queue-length	Scale based on SQS queue length	External	>=1.12

Example

This is an example of an HPA that will scale based on the length of an SQS queue.

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: myapp-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: custom-metrics-consumer
  minReplicas: 1
  maxReplicas: 10
  metrics:
  - type: External
    external:
      metric:
        name: my-sqs
        selector:
          matchLabels:
            type: sqs-queue-length
            queue-name: foobar
            region: eu-central-1
      target:
        averageValue: "30"
        type: AverageValue

The matchLabels are used by kube-metrics-adapter to configure a collector that will get the queue length for an SQS queue named foobar in region eu-central-1.

The AWS account of the queue currently depends on how kube-metrics-adapter is configured to get AWS credentials. The normal assumption is that you run the adapter in a cluster running in the AWS account where the queue is defined. Please open an issue if you would like support for other use cases.

ZMON collector

The ZMON collector allows scaling based on external metrics exposed by ZMON checks.

Supported metrics

Metric	Description	Type	K8s Versions
zmon-check	Scale based on any ZMON check results	External	>=1.12

Example

This is an example of an HPA that will scale based on the specified value exposed by a ZMON check with id 1234.

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: myapp-hpa
  annotations:
    # metric-config.<metricType>.<metricName>.<collectorType>/<configKey>
    metric-config.external.my-zmon-check.zmon/key: "custom.*"
    metric-config.external.my-zmon-check.zmon/tag-application: "my-custom-app-*"
    metric-config.external.my-zmon-check.zmon/interval: "60s" # optional
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: custom-metrics-consumer
  minReplicas: 1
  maxReplicas: 10
  metrics:
  - type: External
    external:
      metric:
          name: my-zmon-check
          selector:
            matchLabels:
              type: zmon
              check-id: "1234" # the ZMON check to query for metrics
              key: "custom.value"
              tag-application: my-custom-app
              aggregators: avg # comma separated list of aggregation functions, default: last
              duration: 5m # default: 10m
      target:
        averageValue: "30"
        type: AverageValue

The check-id specifies the ZMON check to query for the metrics. key specifies the JSON key in the check output to extract the metric value from. E.g. if you have a check which returns the following data:

{
    "custom": {
        "value": 1.0
    },
    "other": {
        "value": 3.0
    }
}

Then the value 1.0 would be returned when the key is defined as custom.value.

The tag-<name> labels defines the tags used for the kariosDB query. In a normal ZMON setup the following tags will be available:

• application
• alias (name of Kubernetes cluster)
• entity - full ZMON entity ID.

aggregators defines the aggregation functions applied to the metrics query. For instance if you define the entity filter type=kube_pod,application=my-custom-app you might get three entities back and then you might want to get an average over the metrics for those three entities. This would be possible by using the avg aggregator. The default aggregator is last which returns only the latest metric point from the query. The supported aggregation functions are avg, count, last, max, min, sum, diff. See the KariosDB docs for details.

The duration defines the duration used for the timeseries query. E.g. if you specify a duration of 5m then the query will return metric points for the last 5 minutes and apply the specified aggregation with the same duration .e.g max(5m).

The annotations metric-config.external.my-zmon-check.zmon/key and metric-config.external.my-zmon-check.zmon/tag-<name> can be optionally used if you need to define a key or other tag with a "star" query syntax like values.*. This hack is in place because it's not allowed to use * in the metric label definitions. If both annotations and corresponding label is defined, then the annotation takes precedence.

Nakadi collector

The Nakadi collector allows scaling based on Nakadi Subscription API stats metrics consumer_lag_seconds or unconsumed_events.

Supported metrics

Metric Type	Description	Type	K8s Versions
unconsumed-events	Scale based on number of unconsumed events for a Nakadi subscription	External	>=1.24
consumer-lag-seconds	Scale based on number of max consumer lag seconds for a Nakadi subscription	External	>=1.24

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: myapp-hpa
  annotations:
    # metric-config.<metricType>.<metricName>.<collectorType>/<configKey>
    metric-config.external.my-nakadi-consumer.nakadi/interval: "60s" # optional
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: custom-metrics-consumer
  minReplicas: 0
  maxReplicas: 8 # should match number of partitions for the event type
  metrics:
  - type: External
    external:
      metric:
          name: my-nakadi-consumer
          selector:
            matchLabels:
              type: nakadi
              subscription-id: "708095f6-cece-4d02-840e-ee488d710b29"
              metric-type: "consumer-lag-seconds|unconsumed-events"
      target:
        # value is compatible with the consumer-lag-seconds metric type.
        # It describes the amount of consumer lag in seconds before scaling
        # additionally up.
        # if an event-type has multiple partitions the value of
        # consumer-lag-seconds is the max of all the partitions.
        value: "600" # 10m
        type: Value
        # averageValue is compatible with unconsumed-events metric type.
        # This means for every 30 unconsumed events a pod is added.
        # unconsumed-events is the sum of of unconsumed_events over all
        # partitions.
        averageValue: "30"
        type: AverageValue

The subscription-id is the Subscription ID of the relevant consumer. The metric-type indicates whether to scale on consumer-lag-seconds or unconsumed-events as outlined below.

unconsumed-events - is the total number of unconsumed events over all partitions. When using this metric-type you should also use the target averageValue which indicates the number of events which can be handled per pod. To best estimate the number of events per pods, you need to understand the average time for processing an event as well as the rate of events.

Example: You have an event type producing 100 events per second between 00:00 and 08:00. Between 08:01 to 23:59 it produces 400 events per second. Let's assume that on average a single pod can consume 100 events per second, then we can define 100 as averageValue and the HPA would scale to 1 between 00:00 and 08:00, and scale to 4 between 08:01 and 23:59. If there for some reason is a short spike of 800 events per second, then it would scale to 8 pods to process those events until the rate goes down again.

consumer-lag-seconds - describes the age of the oldest unconsumed event for a subscription. If the event type has multiple partitions the lag is defined as the max age over all partitions. When using this metric-type you should use the target value to indicate the max lag (in seconds) before the HPA should scale.

Example: You have a subscription with a defined SLO of "99.99 of events are consumed within 30 min.". In this case you can define a target value of e.g. 20 min. (1200s) (to include a safety buffer) such that the HPA only scales up from 1 to 2 if the target of 20 min. is breached and it needs to work faster with more consumers. For this case you should also account for the average time for processing an event when defining the target.

HTTP Collector

The http collector allows collecting metrics from an external endpoint specified in the HPA. Currently only json-path collection is supported.

Supported metrics

Metric	Description	Type	K8s Versions
custom	No predefined metrics. Metrics are generated from user defined queries.	Pods	>=1.12

Example

This is an example of using the HTTP collector to collect metrics from a json metrics endpoint specified in the annotations.

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: myapp-hpa
  annotations:
    # metric-config.<metricType>.<metricName>.<collectorType>/<configKey>
    metric-config.external.unique-metric-name.json-path/json-key: "$.some-metric.value"
    metric-config.external.unique-metric-name.json-path/endpoint: "http://metric-source.app-namespace:8080/metrics"
    metric-config.external.unique-metric-name.json-path/aggregator: "max"
    metric-config.external.unique-metric-name.json-path/interval: "60s" # optional
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: myapp
  minReplicas: 1
  maxReplicas: 10
  metrics:
  - type: External
    external:
      metric:
        name: unique-metric-name
        selector:
          matchLabels:
            type: json-path
      target:
        averageValue: 1
        type: AverageValue

The HTTP collector similar to the Pod Metrics collector. The following configuration values are supported:

• json-key to specify the JSON path of the metric to be queried
• endpoint the fully formed path to query for the metric. In the above example a Kubernetes Service in the namespace app-namespace is called.
• aggregator is only required if the metric is an array of values and specifies how the values are aggregated. Currently this option can support the values: sum, max, min, avg.

Scrape Interval

It's possible to configure the scrape interval for each of the metric types via an annotation:

metric-config.<metricType>.<metricName>.<collectorType>/interval: "30s"

The default is 60s but can be reduced to let the adapter collect metrics more often.

ScalingSchedule Collectors

The ScalingSchedule and ClusterScalingSchedule collectors allow collecting time-based metrics from the respective CRD objects specified in the HPA.

These collectors are disabled by default, you have to start the server with the --scaling-schedule flag to enable it. Remember to deploy the CRDs ScalingSchedule and ClusterScalingSchedule and allow the service account used by the server to read, watch and list them.

Supported metrics

Metric	Description	Type	K8s Versions
ObjectName	The metric is calculated and stored for each ScalingSchedule and ClusterScalingSchedule referenced in the HPAs	ScalingSchedule and ClusterScalingSchedule	>=1.16

Ramp-up and ramp-down feature

To avoid abrupt scaling due to time based metrics,the SchalingSchedule collector has a feature of ramp-up and ramp-down the metric over a specific period of time. The duration of the scaling window can be configured individually in the [Cluster]ScalingSchedule object, via the option scalingWindowDurationMinutes or globally for all scheduled events, and defaults to a globally configured value if not specified. The default for the latter is set to 10 minutes, but can be changed using the --scaling-schedule-default-scaling-window flag.

This spreads the scale events around, creating less load on the other components, and helping the rest of the metrics (like the CPU ones) to adjust as well.

The HPA algorithm does not make changes if the metric change is less than the specified by the horizontal-pod-autoscaler-tolerance flag:

We'll skip scaling if the ratio is sufficiently close to 1.0 (within a globally-configurable tolerance, from the --horizontal-pod-autoscaler-tolerance flag, which defaults to 0.1.

With that in mind, the ramp-up and ramp-down feature divides the scaling over the specified period of time in buckets, trying to achieve changes bigger than the configured tolerance. The number of buckets defaults to 10 and can be configured by the --scaling-schedule-ramp-steps flag.

Important: note that the ramp-up and ramp-down feature can lead to deployments achieving less than the specified number of pods, due to the HPA 10% change rule and the ceiling function applied to the desired number of the pods (check the algorithm details). It varies with the configured metric for ScalingSchedule events, the number of pods and the configured horizontal-pod-autoscaler-tolerance flag of your kubernetes installation. This gist contains the code to simulate the situations a deployment with different number of pods, with a metric of 10000 can face with 10 buckets (max of 90% of the metric returned) and 5 buckets (max of 80% of the metric returned). The ramp-up and ramp-down feature can be disabled by setting --scaling-schedule-default-scaling-window to 0 and abrupt scalings can be handled via scaling policies.

Example

This is an example of using the ScalingSchedule collectors to collect metrics from a deployed kind of the CRD. First, the schedule object:

apiVersion: zalando.org/v1
kind: ClusterScalingSchedule
metadata:
  name: "scheduling-event"
spec:
  schedules:
  - type: OneTime
    date: "2021-10-02T08:08:08+02:00"
    durationMinutes: 30
    value: 100
  - type: Repeating
    durationMinutes: 10
    value: 120
    period:
      startTime: "15:45"
      timezone: "Europe/Berlin"
      days:
      - Mon
      - Wed
      - Fri

This resource defines a scheduling event named scheduling-event with two schedules of the kind ClusterScalingSchedule.

ClusterScalingSchedule objects aren't namespaced, what means it can be referenced by any HPA in any namespace in the cluster. ScalingSchedule have the exact same fields and behavior, but can be referenced just by HPAs in the same namespace. The schedules can have the type Repeating or OneTime.

This example configuration will generate the following result: at 2021-10-02T08:08:08+02:00 for 30 minutes a metric with the value of 100 will be returned. Every Monday, Wednesday and Friday, starting at 15 hours and 45 minutes (Berlin time), a metric with the value of 120 will be returned for 10 minutes. It's not the case of this example, but if multiple schedules collide in time, the biggest value is returned.

Check the CRDs definitions (ScalingSchedule, ClusterScalingSchedule) for a better understanding of the possible fields and their behavior.

An HPA can reference the deployed ClusterScalingSchedule object as this example:

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: "myapp-hpa"
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: myapp
  minReplicas: 1
  maxReplicas: 15
  metrics:
  - type: Object
    object:
      describedObject:
        apiVersion: zalando.org/v1
        kind: ClusterScalingSchedule
        name: "scheduling-event"
      metric:
        name: "scheduling-event"
      target:
        type: AverageValue
        averageValue: "10"

The name of the metric is equal to the name of the referenced object. The target.averageValue in this example is set to 10. This value will be used by the HPA controller to define the desired number of pods, based on the metric obtained (check the HPA algorithm details for more context). This HPA configuration explicitly says that each pod of this application supports 10 units of the ClusterScalingSchedule metric. Multiple applications can share the same ClusterScalingSchedule or ScalingSchedule event and have a different number of pods based on its target.averageValue configuration.

In our specific example at 2021-10-02T08:08:08+02:00 as the metric has the value 100, this application will scale to 10 pods (100/10). Every Monday, Wednesday and Friday, starting at 15 hours and 45 minutes (Berlin time) the application will scale to 12 pods (120/10). Both scaling up will last at least the configured duration times of the schedules. After that, regular HPA scale down behavior applies.

Note that these number of pods are just considering these custom metrics, the normal HPA behavior still applies, such as: in case of multiple metrics the biggest number of pods is the utilized one, HPA max and min replica configuration, autoscaling policies, etc.